Our earlier approach to analyzing case-parents data for multiplicative gene-environment interaction leads to valid inference for a causative SNP under two crucial assumptions. The first assumption is that, conditional on parents' genotypes, the genotype distributions of children reflect Mendelian assortment. The second is that, conditional on parents' genotypes, a child's genotype and exposure are independent. Recently, we have been considering a study design that involves one affected and one unaffected offspring and their parents. We call this structure a tetrad. Our proposal is to genotype the affected offspring and the parents and to collect exposure information from both offspring under that idea that we could test genetic and gene-environment interaction effects using the embedded case-parent-triad design and we could study exposure using the embedded sibling-pair design. In studying this design, we learned that previously proposed family-based tests of gene-environment interaction can be biased when subpopulations differ in both allele frequency and exposure prevalence and when the SNP under study is a marker and not itself the causative SNP. This finding was both surprising and troubling, as researchers had previously believed that family-based studies of gene-environment interaction would robust to bias from population structure even when studying markers. We have published a manuscript that describes these issues in detail and proposes a robust method of analysis for tetrad designs or discordant sib-pairs that can provide valid inference about gene-environment interactions for markers when the exposure under study is dichotomous. In a related manuscript, we have proposed a sibling-augmented case-only design and showed that, with appropriate analysis, it provides the same robust inferences for gene-environment interactions as the analyses we proposed for the tetrad and disease-discordant sib-pair designs. We also found that, for matched designs with one affected and two or more unaffected sibling, treating the sibship as a nuclear family with missing parents is more efficient than traditional conditional logistic regression, and it allows inclusion of unmatched subjects as well. We are studying the application of specimen pooling to DNA from case-parent triads when the genotyping assay counts the number of variant alleles in a pooled specimen. Our procedure partitions a sample of triads into small subsets of, say, two triads each, and, for each subset, constructs three pooled DNA specimens: one each from mothers, from fathers, and from offspring. Treating the individual genotypes that comprise each pool as missing data, our log-linear-modeling approach uses the expectation-maximization algorithm to estimate relative risk parameters for inherited alleles, maternal alleles, or parent-of-origin effects, something other DNA pooling approaches cannot do. We see little loss of power compared to genotyping individuals when genotypes are measured without error, but power declines as genotyping error rates increase. For sufficiently accurate assays, our approach promises to reduce genotyping costs with minimal loss of power. (see also Z01 ES040007 BB; PI Clare Weinberg.)